Table of Contents¶

  • Python Data Visualization Landscape
  • Matplotlib: plotting examples
  • Plotting with pandas
  • Seaborn
  • Plotly Express: Interactive Plots

Python Data Visualization Landscape¶

Data visualization is one of the most important step in the data mining process. The choice of the correct plot depends on three aspects:

  • which data you are expected to plot
  • what is the goal of the visualization
  • for whom the plot is intended

Anaconda Blog: Python Data Visualization 2018: Why So Many Libraries?

dataviz

Most of the libraries fall into the "InfoVis" group, focusing on visualizations of information in arbitrary spaces, not necessarily the three-dimensional physical world.

InfoVis libraries use the two dimensions of the printed page or computer screen to make abstract spaces interpretable, typically with axes and labels. The InfoVis libraries can be further broken down into numerous subgroups:

  • Matplotlib: One of the oldest and by far the most popular of the InfoVis libraries, released in 2003, with a very extensive range of 2D plot types and output formats

  • Matplotlib-based: A variety of tools have built on Matplotlib's 2D-plotting capability over the years, either using it as a rendering engine for a certain type of data or in a certain domain (pandas, NetworkX, Cartopy, yt, etc.), or providing a higher-level API on top to simplify plot creation (ggplot, plotnine, HoloViews, GeoViews), or extending it with additional types of plots (seaborn, etc.).

  • JavaScript: Once HTML5 allowed rich interactivity in browsers, many libraries arose to provide interactive 2D plots for web pages and in Jupyter notebooks, either using custom JS (Bokeh, Toyplot) or primarily wrapping existing JS libraries like D3 (Plotly, bqplot).

The most basic plot types are shared between multiple libraries, but others are only available in certain libraries.

  • Hint: look at the example galleries for each library.

As a rough guide:

  • Statistical plots (scatter plots, lines, areas, bars, histograms): Covered well by nearly all InfoVis libraries, but are the main focus for Seaborn, bqplot, Altair, ggplot2, plotnine
  • Images, regular grids, rectangular meshes: Well supported by Bokeh, Datashader, HoloViews, Matplotlib, Plotly
  • Irregular 2D meshes (triangular grids): Well supported by the SciVis libraries plus Matplotlib, Bokeh, Datashader, HoloViews
  • Geographical data: Matplotlib (with Cartopy), GeoViews, ipyleaflet, Plotly
  • Networks/graphs: NetworkX, Plotly, Bokeh, HoloViews, Datashader
  • 3D (meshes, scatter, etc.): Fully supported by the SciVis libraries, plus some support in Plotly, Matplotlib, HoloViews, and ipyvolume.

Matplotlib: plotting examples¶

Recommended reading: sample plots in Matplotlib

Matplotlib is an excellent 2D and 3D graphics library for generating scientific figures. Some of the many advantages of this library include:

  • Easy to get started
  • Support for $\LaTeX$ formatted labels and texts
  • Great control (programmatically) of every element in a figure, including figure size and DPI.
  • High-quality output in many formats, including PNG, PDF, SVG, EPS, and PGF.
In [1]:
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import os
In [2]:
t = np.arange(0, 10, 0.1)
sin_t = np.sin(2 * np.pi * t / 5)
cos_t = np.cos(2 * np.pi * t / 5)
In [3]:
t
Out[3]:
array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,
       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,
       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,
       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1,
       5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4,
       6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7,
       7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9. ,
       9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9])
In [4]:
sin_t, cos_t
Out[4]:
(array([ 0.00000000e+00,  1.25333234e-01,  2.48689887e-01,  3.68124553e-01,
         4.81753674e-01,  5.87785252e-01,  6.84547106e-01,  7.70513243e-01,
         8.44327926e-01,  9.04827052e-01,  9.51056516e-01,  9.82287251e-01,
         9.98026728e-01,  9.98026728e-01,  9.82287251e-01,  9.51056516e-01,
         9.04827052e-01,  8.44327926e-01,  7.70513243e-01,  6.84547106e-01,
         5.87785252e-01,  4.81753674e-01,  3.68124553e-01,  2.48689887e-01,
         1.25333234e-01,  1.22464680e-16, -1.25333234e-01, -2.48689887e-01,
        -3.68124553e-01, -4.81753674e-01, -5.87785252e-01, -6.84547106e-01,
        -7.70513243e-01, -8.44327926e-01, -9.04827052e-01, -9.51056516e-01,
        -9.82287251e-01, -9.98026728e-01, -9.98026728e-01, -9.82287251e-01,
        -9.51056516e-01, -9.04827052e-01, -8.44327926e-01, -7.70513243e-01,
        -6.84547106e-01, -5.87785252e-01, -4.81753674e-01, -3.68124553e-01,
        -2.48689887e-01, -1.25333234e-01, -2.44929360e-16,  1.25333234e-01,
         2.48689887e-01,  3.68124553e-01,  4.81753674e-01,  5.87785252e-01,
         6.84547106e-01,  7.70513243e-01,  8.44327926e-01,  9.04827052e-01,
         9.51056516e-01,  9.82287251e-01,  9.98026728e-01,  9.98026728e-01,
         9.82287251e-01,  9.51056516e-01,  9.04827052e-01,  8.44327926e-01,
         7.70513243e-01,  6.84547106e-01,  5.87785252e-01,  4.81753674e-01,
         3.68124553e-01,  2.48689887e-01,  1.25333234e-01,  3.67394040e-16,
        -1.25333234e-01, -2.48689887e-01, -3.68124553e-01, -4.81753674e-01,
        -5.87785252e-01, -6.84547106e-01, -7.70513243e-01, -8.44327926e-01,
        -9.04827052e-01, -9.51056516e-01, -9.82287251e-01, -9.98026728e-01,
        -9.98026728e-01, -9.82287251e-01, -9.51056516e-01, -9.04827052e-01,
        -8.44327926e-01, -7.70513243e-01, -6.84547106e-01, -5.87785252e-01,
        -4.81753674e-01, -3.68124553e-01, -2.48689887e-01, -1.25333234e-01]),
 array([ 1.        ,  0.9921147 ,  0.96858316,  0.92977649,  0.87630668,
         0.80901699,  0.72896863,  0.63742399,  0.53582679,  0.42577929,
         0.30901699,  0.18738131,  0.06279052, -0.06279052, -0.18738131,
        -0.30901699, -0.42577929, -0.53582679, -0.63742399, -0.72896863,
        -0.80901699, -0.87630668, -0.92977649, -0.96858316, -0.9921147 ,
        -1.        , -0.9921147 , -0.96858316, -0.92977649, -0.87630668,
        -0.80901699, -0.72896863, -0.63742399, -0.53582679, -0.42577929,
        -0.30901699, -0.18738131, -0.06279052,  0.06279052,  0.18738131,
         0.30901699,  0.42577929,  0.53582679,  0.63742399,  0.72896863,
         0.80901699,  0.87630668,  0.92977649,  0.96858316,  0.9921147 ,
         1.        ,  0.9921147 ,  0.96858316,  0.92977649,  0.87630668,
         0.80901699,  0.72896863,  0.63742399,  0.53582679,  0.42577929,
         0.30901699,  0.18738131,  0.06279052, -0.06279052, -0.18738131,
        -0.30901699, -0.42577929, -0.53582679, -0.63742399, -0.72896863,
        -0.80901699, -0.87630668, -0.92977649, -0.96858316, -0.9921147 ,
        -1.        , -0.9921147 , -0.96858316, -0.92977649, -0.87630668,
        -0.80901699, -0.72896863, -0.63742399, -0.53582679, -0.42577929,
        -0.30901699, -0.18738131, -0.06279052,  0.06279052,  0.18738131,
         0.30901699,  0.42577929,  0.53582679,  0.63742399,  0.72896863,
         0.80901699,  0.87630668,  0.92977649,  0.96858316,  0.9921147 ]))
In [5]:
plt.plot(t, cos_t)
plt.plot(t, sin_t)
Out[5]:
[<matplotlib.lines.Line2D at 0x109ab4510>]
No description has been provided for this image

Pyplot Functions¶

There are many pyplot functions available for us to customize our figures. For example:

Fucntion Description
plt.xlim set $x$ limits
plt.ylim set $y$ limits
plt.grid add grid lines
plt.title add a title
plt.xlabel add label to the horizontal axis
plt.ylabel add label to the vertical axis
plt.axis set axis properties (equal, off, scaled, etc.)
plt.xticks set tick locations on the horizontal axis
plt.yticks set tick locations on the vertical axis
plt.legend display legend for several lines in the same figure
plt.savefig save figure (as .png, .pdf, etc.) to working directory
plt.figure create a new figure and set its properties

See the pyplot documentation for a full list of functions.

In [6]:
plt.figure(figsize = (15, 5)) 
# figsize is a tuple of the width and height of the figure in inches

plt.plot(t, cos_t, '.r', label = r"$cos(\theta)$") 
# ".r" is a format string which denotes linestyle=None, marker = '.' and color = 'red' 

plt.plot(t, sin_t, '--g', label = r"$sin(\theta)$")
# "--g" is a format string which denotes linestyle=dashed and color = 'green' 

plt.title('two functions')
plt.xlabel(r"$\theta$")
plt.ylabel('values')
plt.ylim([-1.1, 1.1])
plt.legend()
plt.grid(axis = 'y')

# plt.legend(loc = 0) # let matplotlib decide the optimal location
# plt.legend(loc = 1) # upper right corner
# plt.legend(loc = 2) # upper left corner
# plt.legend(loc = 3) # lower left corner
# plt.legend(loc = 4) # lower right corner
# # .. many more options are available

plt.savefig(os.path.join('out', 'example_figure.png'), format = 'png')
plt.show()
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Colors¶

Character Color
b blue
g green
r red
c cyan
m magenta
y yellow
k black
w white

Markers¶

Character Marker
. point
o circle
v triangle down
^ triangle up
s square
p pentagon
* star
+ plus
x x
D diamond

Line Styles¶

Character Line Style
- solid line style
-- dashed line style
-. dash-dot line style
: dotted line style

See the matplotlib.pyplot.plot documentation for more options.

Subplots¶

We start by storing a reference to the newly created figure instance and the axes array in the f and axarr variables, respectively

In [7]:
# Two subplots, the axes array is 1-d
f, axarr = plt.subplots(2, sharex = True) 
axarr[0].plot(t, cos_t, '.r')
axarr[1].plot(t, sin_t, '.b')
plt.tight_layout()
plt.show()
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Plotting with pandas¶

Pandas builds on top of Matplotlib but exploits the knowledge included in Dataframes to improve the default output.

Check the user guide

In [8]:
composers_df = pd.read_excel('dataset/composers.xlsx', sheet_name = 'Sheet5')
composers_df = composers_df.dropna()
composers_df
Out[8]:
composer birth death period country
0 Mahler 1860 1911.0 post-romantic Austria
1 Beethoven 1770 1827.0 romantic Germany
2 Puccini 1858 1924.0 post-romantic Italy
3 Shostakovich 1906 1975.0 modern Russia
4 Verdi 1813 1901.0 romantic Italy
5 Dvorak 1841 1904.0 romantic Czechia
6 Schumann 1810 1856.0 romantic Germany
7 Stravinsky 1882 1971.0 modern Russia
8 Sibelius 1865 1957.0 post-romantic Finland
9 Haydn 1732 1809.0 classic Austria
10 Mozart 1756 1791.0 classic Austria
11 Messiaen 1908 1992.0 modern France
12 Prokofiev 1891 1953.0 modern RUssia
13 Monteverdi 1567 1643.0 renaissance Italy
14 Haendel 1685 1759.0 baroque Germany
15 Brahms 1833 1897.0 romantic Germany
16 Purcell 1659 1695.0 baroque England
17 Charpentier 1643 1704.0 baroque France
18 Bruckner 1824 1896.0 post-romantic Austria
19 Berg 1885 1935.0 modern Austria
20 Couperin 1626 1661.0 baroque France
21 Rameau 1683 1764.0 baroque France
22 Berlioz 1803 1869.0 romantic France
23 Gounod 1818 1893.0 romantic France
24 Massenet 1842 1912.0 romantic France
25 Boulez 1925 2016.0 modern France
26 Palestrina 1525 1594.0 renaissance Italy
27 Gesualdo 1566 1613.0 renaissance Italy
28 Caldara 1670 1736.0 baroque Italy
29 Pergolesi 1710 1736.0 baroque Italy
30 Scarlatti 1685 1757.0 baroque Italy
31 Caccini 1587 1640.0 baroque Italy
32 Cimarosa 1749 1801.0 classic Italy
33 Donizetti 1797 1848.0 romantic Italy
34 Leoncavallo 1858 1919.0 romantic Italy
35 Bellini 1801 1835.0 romantic Italy
36 Dufay 1397 1474.0 renaissance Belgium
37 Lassus 1532 1594.0 renaissance Belgium
38 Borodin 1833 1887.0 romantic Russia
39 Mussorsgsky 1839 1881.0 romantic Russia
40 Soler 1754 1806.0 classic Spain
41 Albeniz 1860 1909.0 romantic Spain
42 Granados 1867 1916.0 romantic Spain
43 Dowland 1563 1626.0 renaissance England
44 Byrd 1540 1623.0 renaissance England
45 Walton 1902 1983.0 modern England
47 Bach 1685 1750.0 baroque Germany
48 Wagner 1813 1883.0 romantic Germany
49 Strauss 1864 1949.0 post-romantic Germany
50 Orff 1895 1982.0 modern Germany
51 Dusek 1731 1799.0 classic Czechia
52 Smetana 1824 1884.0 romantic Czechia
53 Janacek 1854 1928.0 modern Czechia
54 Copland 1900 1990.0 modern USA
55 Bernstein 1918 1990.0 modern USA
In [9]:
composers_df.head()
Out[9]:
composer birth death period country
0 Mahler 1860 1911.0 post-romantic Austria
1 Beethoven 1770 1827.0 romantic Germany
2 Puccini 1858 1924.0 post-romantic Italy
3 Shostakovich 1906 1975.0 modern Russia
4 Verdi 1813 1901.0 romantic Italy
  • The plot method on Series and DataFrame is just a simple wrapper around plt.plot()
In [10]:
composers_df.birth.plot()
Out[10]:
<Axes: >
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  • On DataFrame, plot() is a convenience to plot all of the columns with labels
In [11]:
composers_df.plot()
Out[11]:
<Axes: >
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  • Plotting methods allow for a handful of plot styles other than the default line plot. These methods can be provided as the kind keyword argument to plot(), and include:
kind plot
bar / barh bar plots
hist histogram
box boxplot
kde or density density plot
area area plots
scatter scatter plots
hexbin hexagonal bin plots
pie pie plots
  • example of scatter plot
In [12]:
composers_df.plot(kind = 'scatter', x = 'birth', y = 'death')
Out[12]:
<Axes: xlabel='birth', ylabel='death'>
No description has been provided for this image
  • example of scatter plot with customization
In [13]:
composers_df.plot(kind = 'scatter', 
                  x = 'birth', 
                  y = 'death',
                  title = 'Composer birth and death',
                  grid = True,
                  fontsize = 15)
plt.show()
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  • example of histogram plot
In [14]:
composers_df.plot(kind = 'hist')
Out[14]:
<Axes: ylabel='Frequency'>
No description has been provided for this image

We can appreciate the advantage of using Pandas: without specifying anything, Pandas made a histogram of the two numeric columns, labelled the axis and even added a legend to the plot.

We can definitely improve visualization by adding transparency!

In [15]:
composers_df.plot(kind = 'hist', alpha = 0.4)
Out[15]:
<Axes: ylabel='Frequency'>
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  • We can also ask for subplots
In [16]:
composers_df.plot.hist(subplots = True, alpha = 0.5)
Out[16]:
array([<Axes: ylabel='Frequency'>, <Axes: ylabel='Frequency'>],
      dtype=object)
No description has been provided for this image
  • example of boxplot
In [17]:
composers_df.plot(kind = 'box')
Out[17]:
<Axes: >
No description has been provided for this image
In [18]:
composers_df.plot(kind = 'box', 
                  subplots = True, 
                  sharey = True)
plt.show()
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In [19]:
composers_df.groupby('period').mean('numeric_only').plot(kind = 'bar')
plt.show()
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In [20]:
composers_df['period'].value_counts().plot(kind = "bar")
plt.show()
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In [21]:
composers_df['period'].value_counts().plot(kind = "pie")
plt.show()
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Seaborn¶

See the overview and the example gallery for an overview on seaborn plotting options.

Seaborn is tightly integrated with matplotlib.

While you can be productive using only seaborn functions, full customization of your graphics will require some knowledge of matplotlib’s concepts and API.

High quality data visualization products can be obtained by combining the two:

  • Seaborn provides a powerful high-level interface for creating visually appealing plots quickly
  • Matplotlib provides deep customizability
In [22]:
import seaborn as sns
  • example of lmplot: plot data and Linear Model regression fits
In [23]:
g = sns.lmplot(x = "birth", y = "death", data = composers_df)
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In [24]:
g = sns.lmplot(x = "birth", y = "death", data = composers_df, hue = "period") 
# hue = Grouping variable that will produce points with different colors
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  • example of jointplot: Draw a plot of two variables with bivariate and univariate graphs.
In [25]:
g = sns.jointplot(x = "birth", y = "death", data = composers_df)
No description has been provided for this image

Assigning a hue variable will add conditional colors to the scatterplot and draw separate density curves on the marginal axes:

internally, it uses kdeplot(): it plots univariate or bivariate distributions using kernel density estimation.

  • A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analagous to a histogram. KDE represents the data using a continuous probability density curve in one or more dimensions.
In [26]:
g = sns.jointplot(x = "birth", y = "death", hue = "period", data = composers_df)
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In [27]:
composers_df['age'] = composers_df['death'] - composers_df['birth']
In [28]:
sns.pairplot(data = composers_df, hue = 'period')
plt.show()
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Correlation Analysis¶

SciPy is a collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data.

SciPy features includes, but are not limited to:

  • statistics
  • linear algebra
  • fourier transform
  • optimization algorithm
  • ...
In [29]:
from scipy.stats import pearsonr
pearsonr(composers_df.birth, composers_df.death)
Out[29]:
PearsonRResult(statistic=np.float64(0.9921670337454901), pvalue=np.float64(1.452853874622559e-49))

The pearsonr function returns:

  • Pearson product-moment correlation coefficent.
  • The p-value associated with the chosen alternative: it roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.

Pearson correlation coefficient can also be obtained with pandas.DataFrame.corr()

In [30]:
composers_df[['age', 'birth', 'death']].corr()
Out[30]:
age birth death
age 1.000000 0.165466 0.287367
birth 0.165466 1.000000 0.992167
death 0.287367 0.992167 1.000000
In [31]:
f,ax = plt.subplots(figsize=(10, 8))
sns.heatmap(composers_df[['age','birth','death']].corr(), 
            annot=True, 
            linewidths=.5, 
            fmt= '.2f',
            ax=ax,
            vmin=-1, # important: otherwise the color code can be "misleading"
            vmax=1, # important: otherwise the color code can be "misleading"
            cmap = "coolwarm")
plt.show()
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Categorical Data¶

seaborn axes-level functions for plotting categorical data:

  • categorical scatter plots
    • stripplot()
    • swarmplot()
  • distribution plots
    • boxplot()
    • violinplot()
    • boxenplot()
  • estimate plots
    • pointplot()
    • barplot()
    • countplot()
In [32]:
sns.countplot(x = 'period', data = composers_df, hue = 'period', palette = "pastel")
plt.show()
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seaborn also provides a figure-level interface, catplot(), that gives unified higher-level access to the axes-level functions.

In [33]:
sns.catplot(x = "period", 
            y = 'birth', 
            kind = "box", 
            hue = 'period',
            palette = "pastel",
            data = composers_df)
Out[33]:
<seaborn.axisgrid.FacetGrid at 0x10a2b4a10>
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Plotly Express: interactive plots¶

Plotly Express is a terse, consistent, high-level API for creating figures.

In [34]:
import plotly.express as px

ImportError? Install it!

conda install -c plotly plotly_express
In [35]:
df = px.data.iris()
df
Out[35]:
sepal_length sepal_width petal_length petal_width species species_id
0 5.1 3.5 1.4 0.2 setosa 1
1 4.9 3.0 1.4 0.2 setosa 1
2 4.7 3.2 1.3 0.2 setosa 1
3 4.6 3.1 1.5 0.2 setosa 1
4 5.0 3.6 1.4 0.2 setosa 1
... ... ... ... ... ... ...
145 6.7 3.0 5.2 2.3 virginica 3
146 6.3 2.5 5.0 1.9 virginica 3
147 6.5 3.0 5.2 2.0 virginica 3
148 6.2 3.4 5.4 2.3 virginica 3
149 5.9 3.0 5.1 1.8 virginica 3

150 rows × 6 columns

  • example of scatter plot
In [36]:
fig = px.scatter(df, 
                 x="sepal_width", 
                 y="sepal_length", 
                 color="species",
                 size='petal_length', 
                 hover_data=['petal_width'],
                 height=600
                )
fig.show()

Lets add a dimension!

  • example of 3D scatter plot
In [37]:
fig = px.scatter_3d(df, 
                    x="sepal_width", 
                    y="sepal_length", 
                    z="petal_length", 
                    size="petal_width",
                    color="species",
                    height=600)
fig.show()
In [38]:
df
Out[38]:
sepal_length sepal_width petal_length petal_width species species_id
0 5.1 3.5 1.4 0.2 setosa 1
1 4.9 3.0 1.4 0.2 setosa 1
2 4.7 3.2 1.3 0.2 setosa 1
3 4.6 3.1 1.5 0.2 setosa 1
4 5.0 3.6 1.4 0.2 setosa 1
... ... ... ... ... ... ...
145 6.7 3.0 5.2 2.3 virginica 3
146 6.3 2.5 5.0 1.9 virginica 3
147 6.5 3.0 5.2 2.0 virginica 3
148 6.2 3.4 5.4 2.3 virginica 3
149 5.9 3.0 5.1 1.8 virginica 3

150 rows × 6 columns

In [39]:
fig = px.scatter_matrix(df.drop(['species_id'], 
                                axis = 1),
                        dimensions = df.drop(['species_id', 'species'], axis = 1),
                        color = 'species',
                        height = 800,
                        width = 1000) 
fig.show()
  • example of pie chart
In [40]:
df = px.data.gapminder().query("year == 2007").query("continent == 'Europe'")
df.loc[df['pop'] < 2.e6, 'country'] = 'Other countries' # Represent only large countries
fig = px.pie(df, 
             values = 'pop', 
             names = 'country', 
             title = 'Population of European continent', 
             height = 600)
fig.show()
  • example of sunburst charts
In [41]:
df = px.data.gapminder().query("year == 2007")
fig = px.sunburst(df, 
                  path = ['continent', 'country'], 
                  values = 'pop',
                  color = 'lifeExp', 
                  hover_data = ['iso_alpha'],
                  height = 600)
fig.show()
  • example of GeoJSON maps
In [42]:
df = px.data.election()
df
Out[42]:
district Coderre Bergeron Joly total winner result district_id
0 101-Bois-de-Liesse 2481 1829 3024 7334 Joly plurality 101
1 102-Cap-Saint-Jacques 2525 1163 2675 6363 Joly plurality 102
2 11-Sault-au-Récollet 3348 2770 2532 8650 Coderre plurality 11
3 111-Mile-End 1734 4782 2514 9030 Bergeron majority 111
4 112-DeLorimier 1770 5933 3044 10747 Bergeron majority 112
5 113-Jeanne-Mance 1455 3599 2316 7370 Bergeron plurality 113
6 12-Saint-Sulpice 3252 2521 2543 8316 Coderre plurality 12
7 121-La Pointe-aux-Prairies 5456 1760 3330 10546 Coderre majority 121
8 122-Pointe-aux-Trembles 4734 1879 2852 9465 Coderre majority 122
9 123-Rivière-des-Prairies 5737 958 1656 8351 Coderre majority 123
10 13-Ahuntsic 2979 3430 2873 9282 Bergeron plurality 13
11 131-Saint-Édouard 1827 6408 2815 11050 Bergeron majority 131
12 132-Étienne-Desmarteau 2331 5748 2788 10867 Bergeron majority 132
13 133-Vieux-Rosemont 2670 4962 3234 10866 Bergeron plurality 133
14 134-Marie-Victorin 3673 3155 2431 9259 Coderre plurality 134
15 14-Bordeaux-Cartierville 3612 1554 2081 7247 Coderre plurality 14
16 141-Côte-de-Liesse 4308 1320 3959 9587 Coderre plurality 141
17 142-Norman-McLaren 4104 1459 2822 8385 Coderre plurality 142
18 151-Saint-Léonard-Est 3931 882 1641 6454 Coderre majority 151
19 152-Saint-Léonard-Ouest 5387 1184 1908 8479 Coderre majority 152
20 161-Saint-HenriPetite-BourgognePointe-Saint-Ch... 2432 3368 3578 9378 Joly plurality 161
21 162-Saint-PaulÉmard 2566 2092 2438 7096 Coderre plurality 162
22 171-ChamplainL'Île-des-Soeurs 3347 2562 3291 9200 Coderre plurality 171
23 172-Desmarchais-Crawford 2476 2631 2849 7956 Joly plurality 172
24 181-Peter-McGill 1451 754 1894 4099 Joly plurality 181
25 182-Saint-Jacques 1906 2169 2282 6357 Joly plurality 182
26 183-Sainte-Marie 1347 2827 2271 6445 Bergeron plurality 183
27 191-Saint-Michel 3668 984 1220 5872 Coderre majority 191
28 192-François-Perrault 2878 2666 2039 7583 Coderre plurality 192
29 193-Villeray 2201 5819 2782 10802 Bergeron majority 193
30 194-Parc-Extension 2420 1793 1402 5615 Coderre plurality 194
31 21-Ouest 2184 691 1076 3951 Coderre majority 21
32 22-Est 1589 708 1172 3469 Coderre plurality 22
33 23-Centre 2526 851 1286 4663 Coderre majority 23
34 31-Darlington 1873 1182 1232 4287 Coderre plurality 31
35 32-Côte-des-Neiges 1644 1950 1578 5172 Bergeron plurality 32
36 33-Snowdon 1548 1503 1636 4687 Joly plurality 33
37 34-Notre-Dame-de-Grâce 1773 2653 3262 7688 Joly plurality 34
38 35-Loyola 2040 1437 2648 6125 Joly plurality 35
39 41-du Canal 1165 832 1266 3263 Joly plurality 41
40 42-J.-Émery-Provost 1193 653 1157 3003 Coderre plurality 42
41 43-Fort-Rolland 1325 1205 1908 4438 Joly plurality 43
42 51-Sault-Saint-Louis 4201 1642 3717 9560 Coderre plurality 51
43 52-Cecil-P.-Newman 3536 1330 2943 7809 Coderre plurality 52
44 61-Pierre-Foretier 631 258 998 1887 Joly majority 61
45 62-Denis-Benjamin-Viger 595 226 1068 1889 Joly majority 62
46 63-Jacques-Bizard 518 224 690 1432 Joly plurality 63
47 64-Sainte-Geneviève 332 131 326 789 Coderre plurality 64
48 71-Tétreaultville 3694 2589 3454 9737 Coderre plurality 71
49 72-MaisonneuveLongue-Pointe 2746 3250 3139 9135 Bergeron plurality 72
50 73-Hochelaga 1546 3679 2675 7900 Bergeron plurality 73
51 74-Louis-Riel 3509 2178 2338 8025 Coderre plurality 74
52 81-Marie-Clarac 6591 1085 1435 9111 Coderre majority 81
53 82-Ovide-Clermont 6229 780 1051 8060 Coderre majority 82
54 91-Claude-Ryan 996 643 423 2062 Coderre plurality 91
55 92-Joseph-Beaubien 540 833 592 1965 Bergeron plurality 92
56 93-Robert-Bourassa 446 465 419 1330 Bergeron plurality 93
57 94-Jeanne-Sauvé 491 698 489 1678 Bergeron plurality 94
In [43]:
geojson = px.data.election_geojson()
geojson
Out[43]:
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   'geometry': {'type': 'Polygon',
    'coordinates': [[[-73.6168611349556, 45.5357046144078],
      [-73.618922206967, 45.5366652760056],
      [-73.6407295659042, 45.5429686686779],
      [-73.6398673671325, 45.5441488267699],
      [-73.6363671046978, 45.548169415833],
      [-73.6342899238824, 45.5494628161417],
      [-73.6325600663803, 45.5499795021129],
      [-73.6301686544477, 45.5503989164938],
      [-73.6278096771011, 45.5513024018691],
      [-73.6235005117779, 45.5536358848324],
      [-73.6201064769685, 45.5520190517324],
      [-73.6205880272051, 45.5514560807313],
      [-73.6086846729618, 45.5461409843801],
      [-73.6123252903368, 45.5421333051385],
      [-73.6144250141163, 45.5383032859687],
      [-73.6168611349556, 45.5357046144078]]]},
   'properties': {'district': '193-Villeray'},
   'id': '193'},
  {'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': [[[-73.6170925681517, 45.5277209056734],
      [-73.617235572865, 45.5277835686717],
      [-73.6208637881685, 45.5236565043515],
      [-73.6221372911404, 45.5240220054992],
      [-73.6227576775837, 45.5233405909978],
      [-73.6485543103777, 45.5308320827376],
      [-73.6407295659042, 45.5429686686779],
      [-73.618922206967, 45.5366652760056],
      [-73.6168611349556, 45.5357046144078],
      [-73.6182756954315, 45.5346855542873],
      [-73.6215351178101, 45.5311083503899],
      [-73.6190955864224, 45.5306548374863],
      [-73.6174856875378, 45.5297416402976],
      [-73.6170629786343, 45.5290836882431],
      [-73.6170925681517, 45.5277209056734]]]},
   'properties': {'district': '194-Parc-Extension'},
   'id': '194'}]}
In [44]:
fig = px.choropleth_mapbox(df, 
                           geojson = geojson, 
                           color = "winner",
                           locations = "district", 
                           featureidkey = "properties.district",
                           center = {"lat": 45.5517, "lon": -73.7073},
                           mapbox_style = "carto-positron", 
                           zoom = 9,
                           height = 600)
fig.show()
In [45]:
df['coderre-joly'] = df.Coderre - df.Joly
In [46]:
fig = px.choropleth_mapbox(df, 
                           geojson = geojson, 
                           color = "coderre-joly",
                           locations = "district", 
                           featureidkey = "properties.district",
                           center = {"lat": 45.5517, "lon": -73.7073},
                           mapbox_style = "carto-positron", 
                           zoom = 9,
                           height = 600)
fig.show()
  • example of outline symbol maps
In [47]:
df = px.data.gapminder()
df
Out[47]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
... ... ... ... ... ... ... ... ...
1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE 716
1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE 716
1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE 716
1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE 716
1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE 716

1704 rows × 8 columns

In [48]:
fig = px.scatter_geo(df, 
                     locations = "iso_alpha", 
                     color = "continent", 
                     hover_name = "country", 
                     size = "pop",
                     animation_frame = "year", 
                     projection = "natural earth",
                     height = 600)
fig.show()